Method and system of tone ordering for discrete multi-tone systems with tone grouping

Abstract

The present disclosure describes a method and system of tone ordering for discrete multi-tone (DMT) systems with tone grouping. Some illustrative embodiments may include a method, comprising, dividing a plurality of sub-channels within a discrete multi-tone symbol into a plurality of groups, selecting each sub-channel of the plurality of sub-channels in non-sequential sub-channel order from the plurality of groups, and assigning data bits in bit order to each sub-channel in the order in which each sub-channel is selected. Each group comprises an equal number of sub-channels, and each sub-channel comprises a frequency bin with a unique center frequency.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application is a non-provisional application claiming priority to U.S. Provisional Application Ser. No. 60 / 580,207, filed on Jun. 16, 2004, and entitled “Method of tone ordering for DMT systems with tone grouping,” which is hereby incorporated by reference.BACKGROUND[0002]1. Technical Field[0003]The present subject matter relates to discrete multi-tone (DMT) communication systems. More particularly, the subject matter relates to the mapping of data to subcarrier frequencies or “tones” within a DMT signal.[0004]2. Background Information[0005]Discrete multi-tone (DMT) modulation has become a pervasive method for transmitting digital data. This data transmission scheme uses a series of subcarrier frequency bins or “tones,” each with a unique center frequency. These tones are spread out within a frequency range or “band,” each tone potentially carrying one or more bits of a larger unit of digital data. The tones are all modulated togethe...

AI Technical Summary

Benefits of technology

This approach reduces storage requirements and maintains decoder efficiency in noisy conditions by distributing noise-affected bits, improving resource efficiency without compromising performance.

Problems solved by technology

Many convolutional decoding algorithms (e.g., the Viterbi algorithm) tend to perform less efficiently when noise is present if the noise correlates to multiple, adjacent bits within a data stream.

This means that if the noise affects multiple adjacent tones within a DMT signal, and if the noisy tones represent bits that are input in sequence into a decoder at the receiver, the decoder will be more likely to make errors.

Further, as the group size is increased to produce greater resource efficiency, the decoder sensitivity to correlated noise also increases, resulting in a decrease in the performance of the decoder.

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